公开(公告)号：US20210374582A1

公开(公告)日：2021-12-02

申请号：US16914099

申请日：2020-06-26

Applicant: Oracle International Corporation

Inventor： Jean-Baptiste Frederic George Tristan ,  Michael Louis Wick ,  Stephen J. Green

IPC: G06N7/00 ,  G06N20/00

Abstract: A fairness metric of decisions pertaining to a plurality of candidates indicated in a data set is estimated. Using a Hamiltonian Monte Carlo sampling algorithm, sample sets corresponding to random variables of a null model and an alternate model are obtained. A respective kernel density estimator is fitted on at least some sample sets, and importance sampling is implemented on additional samples generated using the kernel density estimators. The estimated fairness metric is provided via one or more programmatic interfaces.

公开(公告)号：US11010768B2

公开(公告)日：2021-05-18

申请号：US14700683

申请日：2015-04-30

Applicant: Oracle International Corporation

Inventor： Pallika Haridas Kanani ,  Michael Louis Wick ,  Adam Craig Pocock

IPC: G06Q30/00 ,  G06F40/284 ,  G06K9/72 ,  G06F16/84

Abstract: A system is provided that extracts attribute values. The system receives data including unstructured text from a data store. The system further tokenizes the unstructured text into tokens, where a token is a character of the unstructured text. The system further annotates the tokens with attribute labels, where an attribute label for a token is determined, in least in part, based on a word that the token originates from within the unstructured text. The system further groups the tokens into text segments based on the attribute labels, where a set of tokens that are annotated with an identical attribute label are grouped into a text segment, and where the text segments define attribute values. The system further stores the attribute labels and the attribute values within the data store.

公开(公告)号：US20200372035A1

公开(公告)日：2020-11-26

申请号：US16781961

申请日：2020-02-04

Applicant: Oracle International Corporation

Inventor： Jean-Baptiste Frederic George Tristan ,  Michael Louis Wick ,  Swetasudha Panda

IPC: G06F16/2457 ,  G06N20/00

Abstract: A Bayesian test of demographic parity for learning to rank may be applied to determine ranking modifications. A fairness control system receiving a ranking of items may apply Bayes factors to determine a likelihood of bias for the ranking. These Bayes factors may include a factor for determining bias in each item and a factor for determining bias in the ranking of the items. An indicator of bias may be generated using the applied Bayes factors and the fairness control system may modify the ranking if the determines likelihood of bias satisfies modification criteria for the ranking.

公开(公告)号：US09779085B2

公开(公告)日：2017-10-03

申请号：US14863996

申请日：2015-09-24

Applicant: Oracle International Corporation

Inventor： Michael Louis Wick ,  Pallika Haridas Kanani ,  Adam Craig Pocock

IPC: G06F17/28 ,  G06F17/27 ,  G10L25/00

CPC classification number: G06F17/2818 ,  G06F17/2735

Abstract: A natural language processing (“NLP”) manager is provided that manages NLP model training. An unlabeled corpus of multilingual documents is provided that span a plurality of target languages. A multilingual embedding is trained on the corpus of multilingual documents as input training data, the multilingual embedding being generalized across the target languages by modifying the input training data and/or transforming multilingual dictionaries into constraints in an underlying optimization problem. An NLP model is trained on training data for a first language of the target languages, using word embeddings of the trained multilingual embedding as features. The trained NLP model is applied for data from a second of the target languages, the first and second languages being different.